DOI: 10.1002/joc.5413
论文题名: Seasonal prediction skill of Indian summer monsoon rainfall in NMME models and monsoon mission CFSv2
作者: Pillai P.A. ; Rao S.A. ; Ramu D.A. ; Pradhan M. ; George G.
刊名: International Journal of Climatology
ISSN: 8998418
出版年: 2018
卷: 38 起始页码: e847
结束页码: e861
语种: 英语
英文关键词: ENSO
; IOD
; ISMR skill
; NMME project
; seasonal prediction
; teleconnections
Scopus关键词: Atmospheric pressure
; Atmospheric thermodynamics
; Climate models
; Climatology
; Forecasting
; Rain
; Surface waters
; Tropics
; ENSO
; ISMR skill
; NMME project
; Seasonal prediction
; Teleconnections
; Oceanography
; climate prediction
; El Nino-Southern Oscillation
; ensemble forecasting
; Indian Ocean Dipole
; modeling
; monsoon
; rainfall
; sea surface temperature
; seasonal variation
; teleconnection
; Indian Ocean
英文摘要: The present study compares the Indian summer monsoon rainfall (ISMR) prediction skill of monsoon mission climate forecast system version 2 (CFSv2-T382) with that of the seasonal prediction models participating in US National Multi-Model Ensemble (NMME) project. In general, the present-day models simulate cooler than observed sea surface temperature (SST) in majority of the Tropics and extratropics. The model rainfall has strong dry bias over major continental regions and wet bias over tropical oceans. Meanwhile, prediction of the boundary forcing such as SST is essential for driving the atmospheric response through teleconnections. It is noted that even though the prediction skill for SST boundary forcings like El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) is not at the best in CFSv2-T382 compared to a few of the NMME models, it shows better skill for ISMR hindcasts initialized at 3-month lead time (February IC). This may be attributed to the better teleconnection pattern of ENSO and IOD in CFSv2-T382, which has minimum biases in equatorial Indo-Pacific region. It also has a better ISMR–SST teleconnections in the Tropics with a pattern correlation of around 0.6. In many of the NMME models, the better prediction skill of the inter-annual variability of SST indices is not transformed into the improvement of ISMR skill through teleconnections. It is therefore concluded that having good prediction skill for major SST boundary forcings is not sufficient, but capturing the appropriate teleconnections of these SST boundary forcings in the model is critical for the better prediction of ISMR. The study points out that the present-day seasonal prediction systems need to be improved in their simulation of tropical SST–monsoon teleconnections, which can improve the seasonal prediction skill of Indian summer monsoon further. One area where the immediate focus is required is the Indian Ocean SST and ISMR teleconnection. © 2018 Royal Meteorological Society Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/116981
Appears in Collections: 气候减缓与适应
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作者单位: Monsoon Mission Program, Indian Institute of Tropical Meteorology, Pune, India
Recommended Citation:
Pillai P.A.,Rao S.A.,Ramu D.A.,et al. Seasonal prediction skill of Indian summer monsoon rainfall in NMME models and monsoon mission CFSv2[J]. International Journal of Climatology,2018-01-01,38